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1. Field of the Invention
This invention generally relates to digital image processing and, more particularly, to systems and methods for detecting defects in a semiconductor device using image comparison techniques.
2. Description of the Related Art
Image comparison techniques are used to detect defects in a semiconductor wafer. Typically, a test image is acquired and then compared to a reference image. A defect-detection algorithm is then used to detect variations between the images and to determine whether such variations are real defects. In the so-called random-logic inspection mode, an image of a first die is acquired and then compared to the image of a second die in the same wafer. Array-inspection mode is similarly performed except that a section of a die is compared to another section in the same die having an identical structure. Array-inspection mode is used, for example, in testing devices with repeating structures such as memory cells. In lieu of comparing images from a wafer being tested, defects may also be detected by comparing an acquired test image with a known good image from a database.
FIG. 1 illustrates a defect detection method in the prior art. A test image and a reference image of the wafer feature being analyzed are acquired from different sections of the wafer using, for example, conventional electron-beam imaging techniques (step 110). Each image comprises a plurality of pixels, with each pixel being defined by its location within the image and its intensity or gray level. The use of gray levels in image processing is known in the art and is described in R. C. Gonzales and R. E. Woods, xe2x80x9cDigital Image Processing,xe2x80x9d Addison-Wesley (1992), e.g. pages 6-7, which is incorporated herein by reference in its entirety. The two images are then aligned pixel-by-pixel such that each feature in the test image matches up with the corresponding feature in the reference image (step 120). A difference image is then generated by subtracting the gray levels of the two images (step 130). Because matching pixels with identical gray levels will be subtracted out, the difference image represents pixel gray level variations between the reference image and the test image. The gray level of each pixel in the difference image is scaled, normalized, and then plotted in a one dimensional histogram such as histogram 200 shown in FIG. 2 (step 140). Histogram 200 plots the number of pixels in the difference image having a specific gray level. For instance, histogram 200 indicates that there are 20,000 pixels in the difference image having a gray level of 50.
A pixel from the test image can be different from a corresponding pixel in the reference image even if there are no defects in the two images. Intensity variations can be caused by, for example, differences in the physical layer structures, noise in the image acquisition electronics and signal paths, and varying noise modulation level within a single image across different gray levels. Thus, pixels in the difference image do not necessarily indicate that a defect exists. To differentiate real defects from false or xe2x80x9cnuisancexe2x80x9d defects, each pixel in the difference image is compared to a threshold window (FIG. 1, step 150). Pixels with a gray level outside the threshold window are declared defects. For example, if the threshold window is xc2x150 and a pixel in the difference image has a gray level of 60 (i.e. the gray levels of the test and reference images differ by 60 units), a defect event is declared (FIG. 1, step 160). The defect event is then verified by an operator to ensure that the die is indeed defective before the die is discarded in subsequent processing.
Finding the optimum threshold value for a given test image is an important but imprecise task. The threshold value must be chosen such that real defects are detected while differentiating nuisance defects. The narrower the threshold value, the more nuisance defects will be declared. Nuisance defects adversely affect production throughput because each defect event must be checked and verified. On the other hand, widening the threshold window will reduce nuisance defect events at the expense of letting real defects go undetected.
From the foregoing, a defect detection method which can detect real defects while minimizing the reporting of nuisance defects is highly desirable.
A novel method and associated apparatus for detecting defects is disclosed. In an embodiment of the invention, a first image and a second image are provided and aligned. A first two-dimensional scatter plot is created by plotting the gray levels of the pixels from the first image against the gray levels of corresponding pixels from the second image. A second two-dimensional scatter plot is then created by filtering the data points of the first scatter plot. The second scatter plot provides a mask shape which can be extracted and filled-in to create a mask. Defects are identified by comparing the gray levels of corresponding pixels from the first and second images against the mask.